Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations4,063
Missing cells52,098
Missing cells (%)35.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory288.0 B

Variable types

Text1
Boolean29
Numeric1
Categorical5

Alerts

biogas_used_for_cooking has constant value "False" Constant
aware_of_no_of_units_generated_by_solar_system is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 3 other fieldsHigh correlation
boil_water_before_drinking is highly overall correlated with source_of_energy_for_boiling_drinking_waterHigh correlation
coconut_shells_or_charcoal_used_for_cooking is highly overall correlated with aware_of_no_of_units_generated_by_solar_system and 14 other fieldsHigh correlation
does_water_heating_equipment_serve_other_housing_units is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 1 other fieldsHigh correlation
firewood_used_for_cooking is highly overall correlated with source_of_energy_for_boiling_drinking_waterHigh correlation
gas_used_for_cooking is highly overall correlated with source_of_energy_for_boiling_drinking_waterHigh correlation
generate_electicity_using_mini_hydropower is highly overall correlated with no_of_units_generated_by_solar_systemHigh correlation
generate_electicity_using_solar_energy is highly overall correlated with aware_of_no_of_units_generated_by_solar_system and 12 other fieldsHigh correlation
generate_electicity_using_wind_power is highly overall correlated with no_of_units_generated_by_solar_systemHigh correlation
household_members_used_hot_water_last_week is highly overall correlated with coconut_shells_or_charcoal_used_for_cookingHigh correlation
no_of_units_generated_by_solar_system is highly overall correlated with aware_of_no_of_units_generated_by_solar_system and 6 other fieldsHigh correlation
sawdust_or_paddy_husk_used_for_cooking is highly overall correlated with aware_of_no_of_units_generated_by_solar_system and 13 other fieldsHigh correlation
solar_energy_used_for_agricultural_systems is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 4 other fieldsHigh correlation
solar_energy_used_for_all_above is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 2 other fieldsHigh correlation
solar_energy_used_for_car_charging is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 2 other fieldsHigh correlation
solar_energy_used_for_cooking is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 2 other fieldsHigh correlation
solar_energy_used_for_other_purposes is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 2 other fieldsHigh correlation
solar_energy_used_for_outdoor_lighting is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 3 other fieldsHigh correlation
solar_energy_used_for_water_heating is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 3 other fieldsHigh correlation
solar_system_invertor_or_noninvertor is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 2 other fieldsHigh correlation
solar_system_ongrid_or_offgird is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 4 other fieldsHigh correlation
source_of_energy_for_boiling_drinking_water is highly overall correlated with boil_water_before_drinking and 5 other fieldsHigh correlation
water_heating_method_for_bathing is highly overall correlated with generate_electicity_using_solar_energyHigh correlation
when_was_solar_system_installed is highly overall correlated with coconut_shells_or_charcoal_used_for_cooking and 2 other fieldsHigh correlation
have_backup_generator is highly imbalanced (81.7%) Imbalance
generate_electicity_using_solar_energy is highly imbalanced (53.2%) Imbalance
generate_electicity_using_bio_energy is highly imbalanced (95.1%) Imbalance
generate_electicity_using_mini_hydropower is highly imbalanced (97.9%) Imbalance
generate_electicity_using_wind_power is highly imbalanced (97.5%) Imbalance
generate_electicity_using_other_methods is highly imbalanced (96.5%) Imbalance
solar_energy_used_for_water_heating is highly imbalanced (60.1%) Imbalance
solar_energy_used_for_cooking is highly imbalanced (82.0%) Imbalance
solar_energy_used_for_outdoor_lighting is highly imbalanced (59.3%) Imbalance
solar_energy_used_for_car_charging is highly imbalanced (93.7%) Imbalance
solar_energy_used_for_agricultural_systems is highly imbalanced (97.5%) Imbalance
solar_energy_used_for_all_above is highly imbalanced (86.0%) Imbalance
solar_energy_used_for_other_purposes is highly imbalanced (78.3%) Imbalance
have_system_to_store_backup_energy is highly imbalanced (61.6%) Imbalance
method_of_receiving_water is highly imbalanced (72.2%) Imbalance
does_water_heating_equipment_serve_other_housing_units is highly imbalanced (57.0%) Imbalance
electricity_generated_using_solar_energy_used_for_cooking is highly imbalanced (90.9%) Imbalance
kerosene_used_for_cooking is highly imbalanced (89.1%) Imbalance
sawdust_or_paddy_husk_used_for_cooking is highly imbalanced (97.9%) Imbalance
coconut_shells_or_charcoal_used_for_cooking is highly imbalanced (99.7%) Imbalance
other_methods_used_for_cooking is highly imbalanced (90.0%) Imbalance
solar_system_ongrid_or_offgird has 3658 (90.0%) missing values Missing
solar_system_invertor_or_noninvertor has 3658 (90.0%) missing values Missing
solar_energy_used_for_water_heating has 3658 (90.0%) missing values Missing
solar_energy_used_for_cooking has 3658 (90.0%) missing values Missing
solar_energy_used_for_outdoor_lighting has 3658 (90.0%) missing values Missing
solar_energy_used_for_car_charging has 3658 (90.0%) missing values Missing
solar_energy_used_for_agricultural_systems has 3658 (90.0%) missing values Missing
solar_energy_used_for_all_above has 3658 (90.0%) missing values Missing
solar_energy_used_for_other_purposes has 3658 (90.0%) missing values Missing
aware_of_no_of_units_generated_by_solar_system has 3658 (90.0%) missing values Missing
no_of_units_generated_by_solar_system has 3868 (95.2%) missing values Missing
when_was_solar_system_installed has 3658 (90.0%) missing values Missing
does_water_heating_equipment_serve_other_housing_units has 3302 (81.3%) missing values Missing
household_members_used_hot_water_last_week has 2457 (60.5%) missing values Missing
source_of_energy_for_boiling_drinking_water has 2233 (55.0%) missing values Missing
household_ID has unique values Unique

Reproduction

Analysis started2024-12-06 05:55:00.385407
Analysis finished2024-12-06 05:55:04.072929
Duration3.69 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

household_ID
Text

Unique 

Distinct4063
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
2024-12-06T11:25:04.268354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters24,378
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4,063 ?
Unique (%)100.0%

Sample

1st rowID0001
2nd rowID0002
3rd rowID0003
4th rowID0004
5th rowID0005
ValueCountFrequency (%)
id0039 1
 
< 0.1%
id4063 1
 
< 0.1%
id0001 1
 
< 0.1%
id0002 1
 
< 0.1%
id0003 1
 
< 0.1%
id0004 1
 
< 0.1%
id0005 1
 
< 0.1%
id0006 1
 
< 0.1%
id0007 1
 
< 0.1%
id0008 1
 
< 0.1%
Other values (4053) 4053
99.8%
2024-12-06T11:25:04.598618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 4063
16.7%
D 4063
16.7%
0 2277
9.3%
3 2217
9.1%
2 2217
9.1%
1 2217
9.1%
4 1280
 
5.3%
5 1216
 
5.0%
6 1210
 
5.0%
7 1206
 
4.9%
Other values (2) 2412
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16252
66.7%
Uppercase Letter 8126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2277
14.0%
3 2217
13.6%
2 2217
13.6%
1 2217
13.6%
4 1280
7.9%
5 1216
7.5%
6 1210
7.4%
7 1206
7.4%
8 1206
7.4%
9 1206
7.4%
Uppercase Letter
ValueCountFrequency (%)
I 4063
50.0%
D 4063
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16252
66.7%
Latin 8126
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2277
14.0%
3 2217
13.6%
2 2217
13.6%
1 2217
13.6%
4 1280
7.9%
5 1216
7.5%
6 1210
7.4%
7 1206
7.4%
8 1206
7.4%
9 1206
7.4%
Latin
ValueCountFrequency (%)
I 4063
50.0%
D 4063
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 4063
16.7%
D 4063
16.7%
0 2277
9.3%
3 2217
9.1%
2 2217
9.1%
1 2217
9.1%
4 1280
 
5.3%
5 1216
 
5.0%
6 1210
 
5.0%
7 1206
 
4.9%
Other values (2) 2412
9.9%

have_backup_generator
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
3950 
True
 
113
ValueCountFrequency (%)
False 3950
97.2%
True 113
 
2.8%
2024-12-06T11:25:04.700055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

generate_electicity_using_solar_energy
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
3658 
True
405 
ValueCountFrequency (%)
False 3658
90.0%
True 405
 
10.0%
2024-12-06T11:25:04.775580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

generate_electicity_using_bio_energy
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4041 
True
 
22
ValueCountFrequency (%)
False 4041
99.5%
True 22
 
0.5%
2024-12-06T11:25:04.851224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

generate_electicity_using_mini_hydropower
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4055 
True
 
8
ValueCountFrequency (%)
False 4055
99.8%
True 8
 
0.2%
2024-12-06T11:25:04.925182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

generate_electicity_using_wind_power
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4053 
True
 
10
ValueCountFrequency (%)
False 4053
99.8%
True 10
 
0.2%
2024-12-06T11:25:05.000532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

generate_electicity_using_other_methods
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4048 
True
 
15
ValueCountFrequency (%)
False 4048
99.6%
True 15
 
0.4%
2024-12-06T11:25:05.074789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_system_ongrid_or_offgird
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
True
 
330
False
 
75
(Missing)
3658 
ValueCountFrequency (%)
True 330
 
8.1%
False 75
 
1.8%
(Missing) 3658
90.0%
2024-12-06T11:25:05.151524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_system_invertor_or_noninvertor
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
True
 
308
False
 
97
(Missing)
3658 
ValueCountFrequency (%)
True 308
 
7.6%
False 97
 
2.4%
(Missing) 3658
90.0%
2024-12-06T11:25:05.228872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_energy_used_for_water_heating
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
False
373 
True
 
32
(Missing)
3658 
ValueCountFrequency (%)
False 373
 
9.2%
True 32
 
0.8%
(Missing) 3658
90.0%
2024-12-06T11:25:05.307176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_energy_used_for_cooking
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
False
394 
True
 
11
(Missing)
3658 
ValueCountFrequency (%)
False 394
 
9.7%
True 11
 
0.3%
(Missing) 3658
90.0%
2024-12-06T11:25:05.381739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_energy_used_for_outdoor_lighting
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
False
372 
True
 
33
(Missing)
3658 
ValueCountFrequency (%)
False 372
 
9.2%
True 33
 
0.8%
(Missing) 3658
90.0%
2024-12-06T11:25:05.459085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_energy_used_for_car_charging
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
False
402 
True
 
3
(Missing)
3658 
ValueCountFrequency (%)
False 402
 
9.9%
True 3
 
0.1%
(Missing) 3658
90.0%
2024-12-06T11:25:05.535168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_energy_used_for_agricultural_systems
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
False
404 
True
 
1
(Missing)
3658 
ValueCountFrequency (%)
False 404
 
9.9%
True 1
 
< 0.1%
(Missing) 3658
90.0%
2024-12-06T11:25:05.612518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_energy_used_for_all_above
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
False
397 
True
 
8
(Missing)
3658 
ValueCountFrequency (%)
False 397
 
9.8%
True 8
 
0.2%
(Missing) 3658
90.0%
2024-12-06T11:25:05.687265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

solar_energy_used_for_other_purposes
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
False
391 
True
 
14
(Missing)
3658 
ValueCountFrequency (%)
False 391
 
9.6%
True 14
 
0.3%
(Missing) 3658
90.0%
2024-12-06T11:25:05.762514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

aware_of_no_of_units_generated_by_solar_system
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size8.1 KiB
False
 
210
True
 
195
(Missing)
3658 
ValueCountFrequency (%)
False 210
 
5.2%
True 195
 
4.8%
(Missing) 3658
90.0%
2024-12-06T11:25:05.837436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

no_of_units_generated_by_solar_system
Real number (ℝ)

High correlation  Missing 

Distinct102
Distinct (%)52.3%
Missing3868
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean445.34718
Minimum0
Maximum2500
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-12-06T11:25:05.933342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1240
median400
Q3600
95-th percentile1045
Maximum2500
Range2500
Interquartile range (IQR)360

Descriptive statistics

Standard deviation381.12807
Coefficient of variation (CV)0.8557999
Kurtosis8.4346225
Mean445.34718
Median Absolute Deviation (MAD)180
Skewness2.3187772
Sum86842.7
Variance145258.61
MonotonicityNot monotonic
2024-12-06T11:25:06.048389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
0.5%
600 14
 
0.3%
500 9
 
0.2%
450 8
 
0.2%
400 8
 
0.2%
300 8
 
0.2%
550 6
 
0.1%
350 4
 
0.1%
650 3
 
0.1%
275 3
 
0.1%
Other values (92) 110
 
2.7%
(Missing) 3868
95.2%
ValueCountFrequency (%)
0 22
0.5%
42 1
 
< 0.1%
43 1
 
< 0.1%
60 2
 
< 0.1%
75 2
 
< 0.1%
85 1
 
< 0.1%
90 1
 
< 0.1%
100 1
 
< 0.1%
102 1
 
< 0.1%
105 1
 
< 0.1%
ValueCountFrequency (%)
2500 1
 
< 0.1%
2200 1
 
< 0.1%
2000 1
 
< 0.1%
1830 1
 
< 0.1%
1813 1
 
< 0.1%
1700 1
 
< 0.1%
1200 3
0.1%
1150 1
 
< 0.1%
1000 1
 
< 0.1%
900 3
0.1%

when_was_solar_system_installed
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing3658
Missing (%)90.0%
Memory size31.9 KiB
Before November 2022
306 
After November 2022
99 

Length

Max length20
Median length20
Mean length19.755556
Min length19

Characters and Unicode

Total characters8,001
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBefore November 2022
2nd rowBefore November 2022
3rd rowBefore November 2022
4th rowBefore November 2022
5th rowAfter November 2022

Common Values

ValueCountFrequency (%)
Before November 2022 306
 
7.5%
After November 2022 99
 
2.4%
(Missing) 3658
90.0%

Length

2024-12-06T11:25:06.156584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-06T11:25:06.241769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
november 405
33.3%
2022 405
33.3%
before 306
25.2%
after 99
 
8.1%

Most occurring characters

ValueCountFrequency (%)
e 1521
19.0%
2 1215
15.2%
r 810
10.1%
810
10.1%
o 711
8.9%
f 405
 
5.1%
b 405
 
5.1%
N 405
 
5.1%
m 405
 
5.1%
v 405
 
5.1%
Other values (4) 909
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4761
59.5%
Decimal Number 1620
 
20.2%
Space Separator 810
 
10.1%
Uppercase Letter 810
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1521
31.9%
r 810
17.0%
o 711
14.9%
f 405
 
8.5%
b 405
 
8.5%
m 405
 
8.5%
v 405
 
8.5%
t 99
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
N 405
50.0%
B 306
37.8%
A 99
 
12.2%
Decimal Number
ValueCountFrequency (%)
2 1215
75.0%
0 405
 
25.0%
Space Separator
ValueCountFrequency (%)
810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5571
69.6%
Common 2430
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1521
27.3%
r 810
14.5%
o 711
12.8%
f 405
 
7.3%
b 405
 
7.3%
N 405
 
7.3%
m 405
 
7.3%
v 405
 
7.3%
B 306
 
5.5%
A 99
 
1.8%
Common
ValueCountFrequency (%)
2 1215
50.0%
810
33.3%
0 405
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1521
19.0%
2 1215
15.2%
r 810
10.1%
810
10.1%
o 711
8.9%
f 405
 
5.1%
b 405
 
5.1%
N 405
 
5.1%
m 405
 
5.1%
v 405
 
5.1%
Other values (4) 909
11.4%

have_system_to_store_backup_energy
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
3759 
True
 
304
ValueCountFrequency (%)
False 3759
92.5%
True 304
 
7.5%
2024-12-06T11:25:06.319776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

method_of_receiving_water
Categorical

Imbalance 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
Tap Water (National Water Supply and drainage board)
3448 
Protected well
375 
Tube well
 
108
Tap Water (Local Government Organizations)
 
75
Tap Water (Community-based water supply and management organization)
 
17
Other values (4)
 
40

Length

Max length68
Median length52
Mean length46.905489
Min length5

Characters and Unicode

Total characters190,577
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTap Water (National Water Supply and drainage board)
2nd rowTap Water (National Water Supply and drainage board)
3rd rowTap Water (National Water Supply and drainage board)
4th rowTap Water (National Water Supply and drainage board)
5th rowTap Water (Local Government Organizations)

Common Values

ValueCountFrequency (%)
Tap Water (National Water Supply and drainage board) 3448
84.9%
Protected well 375
 
9.2%
Tube well 108
 
2.7%
Tap Water (Local Government Organizations) 75
 
1.8%
Tap Water (Community-based water supply and management organization) 17
 
0.4%
Tap Water (Private Water Projects) 14
 
0.3%
Other 11
 
0.3%
Unprotected well 9
 
0.2%
Bottled water 6
 
0.1%

Length

2024-12-06T11:25:06.414566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-06T11:25:06.521086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
water 7039
24.1%
tap 3554
12.2%
supply 3465
11.9%
and 3465
11.9%
national 3448
11.8%
drainage 3448
11.8%
board 3448
11.8%
well 492
 
1.7%
protected 375
 
1.3%
tube 108
 
0.4%
Other values (11) 330
 
1.1%

Most occurring characters

ValueCountFrequency (%)
a 31622
16.6%
25109
13.2%
r 14525
 
7.6%
e 12101
 
6.3%
t 11507
 
6.0%
d 10768
 
5.7%
n 10755
 
5.6%
p 10493
 
5.5%
l 7978
 
4.2%
o 7576
 
4.0%
Other values (27) 48143
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140098
73.5%
Space Separator 25109
 
13.2%
Uppercase Letter 18245
 
9.6%
Open Punctuation 3554
 
1.9%
Close Punctuation 3554
 
1.9%
Dash Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 31622
22.6%
r 14525
10.4%
e 12101
 
8.6%
t 11507
 
8.2%
d 10768
 
7.7%
n 10755
 
7.7%
p 10493
 
7.5%
l 7978
 
5.7%
o 7576
 
5.4%
i 7111
 
5.1%
Other values (12) 15662
11.2%
Uppercase Letter
ValueCountFrequency (%)
W 7016
38.5%
T 3662
20.1%
N 3448
18.9%
S 3448
18.9%
P 403
 
2.2%
O 86
 
0.5%
G 75
 
0.4%
L 75
 
0.4%
C 17
 
0.1%
U 9
 
< 0.1%
Space Separator
ValueCountFrequency (%)
25109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3554
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3554
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 158343
83.1%
Common 32234
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 31622
20.0%
r 14525
9.2%
e 12101
 
7.6%
t 11507
 
7.3%
d 10768
 
6.8%
n 10755
 
6.8%
p 10493
 
6.6%
l 7978
 
5.0%
o 7576
 
4.8%
i 7111
 
4.5%
Other values (23) 33907
21.4%
Common
ValueCountFrequency (%)
25109
77.9%
( 3554
 
11.0%
) 3554
 
11.0%
- 17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 31622
16.6%
25109
13.2%
r 14525
 
7.6%
e 12101
 
6.3%
t 11507
 
6.0%
d 10768
 
5.7%
n 10755
 
5.6%
p 10493
 
5.5%
l 7978
 
4.2%
o 7576
 
4.0%
Other values (27) 48143
25.3%

water_heating_method_for_bathing
Categorical

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
None, we do not use hot water for bathing or body wash purposes.
2457 
We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).
746 
We have an in-built water heating system powered solely by the grid supply.
507 
We have an in-built water heating system powered solely by solar energy.
 
188
We don't have an inbuilt system for water heating, we use heated water through an electric kettle/heater.
 
99
Other values (2)
 
66

Length

Max length212
Median length64
Mean length94.148659
Min length61

Characters and Unicode

Total characters382,526
Distinct characters32
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWe don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).
2nd rowNone, we do not use hot water for bathing or body wash purposes.
3rd rowWe have an in-built water heating system powered solely by the grid supply.
4th rowWe don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).
5th rowNone, we do not use hot water for bathing or body wash purposes.

Common Values

ValueCountFrequency (%)
None, we do not use hot water for bathing or body wash purposes. 2457
60.5%
We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance). 746
 
18.4%
We have an in-built water heating system powered solely by the grid supply. 507
 
12.5%
We have an in-built water heating system powered solely by solar energy. 188
 
4.6%
We don't have an inbuilt system for water heating, we use heated water through an electric kettle/heater. 99
 
2.4%
We have an in-built water heating system powered both by solar and the grid supply. 52
 
1.3%
We have a water heating system powered by a different source. 14
 
0.3%

Length

2024-12-06T11:25:06.652198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-06T11:25:06.760303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
water 6400
 
9.2%
we 4908
 
7.1%
use 3302
 
4.8%
for 3302
 
4.8%
or 3203
 
4.6%
do 2457
 
3.5%
none 2457
 
3.5%
hot 2457
 
3.5%
not 2457
 
3.5%
bathing 2457
 
3.5%
Other values (41) 36115
52.0%

Most occurring characters

ValueCountFrequency (%)
65452
17.1%
e 43248
11.3%
t 29591
 
7.7%
o 26937
 
7.0%
a 24941
 
6.5%
r 22657
 
5.9%
s 19123
 
5.0%
n 17089
 
4.5%
h 16812
 
4.4%
w 13666
 
3.6%
Other values (22) 103010
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 300225
78.5%
Space Separator 65452
 
17.1%
Other Punctuation 10547
 
2.8%
Uppercase Letter 4063
 
1.1%
Dash Punctuation 747
 
0.2%
Open Punctuation 746
 
0.2%
Close Punctuation 746
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 43248
14.4%
t 29591
9.9%
o 26937
 
9.0%
a 24941
 
8.3%
r 22657
 
7.5%
s 19123
 
6.4%
n 17089
 
5.7%
h 16812
 
5.6%
w 13666
 
4.6%
i 12395
 
4.1%
Other values (12) 73766
24.6%
Other Punctuation
ValueCountFrequency (%)
. 4809
45.6%
, 4794
45.5%
' 845
 
8.0%
/ 99
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
N 2457
60.5%
W 1606
39.5%
Space Separator
ValueCountFrequency (%)
65452
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 747
100.0%
Open Punctuation
ValueCountFrequency (%)
( 746
100.0%
Close Punctuation
ValueCountFrequency (%)
) 746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 304288
79.5%
Common 78238
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 43248
14.2%
t 29591
 
9.7%
o 26937
 
8.9%
a 24941
 
8.2%
r 22657
 
7.4%
s 19123
 
6.3%
n 17089
 
5.6%
h 16812
 
5.5%
w 13666
 
4.5%
i 12395
 
4.1%
Other values (14) 77829
25.6%
Common
ValueCountFrequency (%)
65452
83.7%
. 4809
 
6.1%
, 4794
 
6.1%
' 845
 
1.1%
- 747
 
1.0%
( 746
 
1.0%
) 746
 
1.0%
/ 99
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 382526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65452
17.1%
e 43248
11.3%
t 29591
 
7.7%
o 26937
 
7.0%
a 24941
 
6.5%
r 22657
 
5.9%
s 19123
 
5.0%
n 17089
 
4.5%
h 16812
 
4.4%
w 13666
 
3.6%
Other values (22) 103010
26.9%

does_water_heating_equipment_serve_other_housing_units
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing3302
Missing (%)81.3%
Memory size8.1 KiB
False
694 
True
 
67
(Missing)
3302 
ValueCountFrequency (%)
False 694
 
17.1%
True 67
 
1.6%
(Missing) 3302
81.3%
2024-12-06T11:25:06.887419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

household_members_used_hot_water_last_week
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing2457
Missing (%)60.5%
Memory size8.1 KiB
True
1014 
False
592 
(Missing)
2457 
ValueCountFrequency (%)
True 1014
25.0%
False 592
 
14.6%
(Missing) 2457
60.5%
2024-12-06T11:25:06.962250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

boil_water_before_drinking
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
2233 
True
1830 
ValueCountFrequency (%)
False 2233
55.0%
True 1830
45.0%
2024-12-06T11:25:07.039914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

source_of_energy_for_boiling_drinking_water
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.4%
Missing2233
Missing (%)55.0%
Memory size31.9 KiB
Gas
1102 
Electricity (directly from the national grid)
382 
Firewood
305 
Electricity (generated from solar energy system)
 
24
Kerosene
 
10
Other values (3)
 
7

Length

Max length48
Median length3
Mean length13.244262
Min length3

Characters and Unicode

Total characters24,237
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowFirewood
2nd rowElectricity (directly from the national grid)
3rd rowGas
4th rowElectricity (directly from the national grid)
5th rowElectricity (directly from the national grid)

Common Values

ValueCountFrequency (%)
Gas 1102
27.1%
Electricity (directly from the national grid) 382
 
9.4%
Firewood 305
 
7.5%
Electricity (generated from solar energy system) 24
 
0.6%
Kerosene 10
 
0.2%
Other 5
 
0.1%
Saw dust/ Paddy husk. 1
 
< 0.1%
Coconut shells/charcoal 1
 
< 0.1%
(Missing) 2233
55.0%

Length

2024-12-06T11:25:07.128995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-06T11:25:07.444660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gas 1102
28.5%
electricity 406
 
10.5%
from 406
 
10.5%
directly 382
 
9.9%
the 382
 
9.9%
national 382
 
9.9%
grid 382
 
9.9%
firewood 305
 
7.9%
generated 24
 
0.6%
solar 24
 
0.6%
Other values (10) 69
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i 2263
 
9.3%
2034
 
8.4%
t 2013
 
8.3%
r 1969
 
8.1%
a 1918
 
7.9%
e 1655
 
6.8%
o 1435
 
5.9%
l 1197
 
4.9%
c 1197
 
4.9%
s 1188
 
4.9%
Other values (22) 7368
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19557
80.7%
Space Separator 2034
 
8.4%
Uppercase Letter 1831
 
7.6%
Open Punctuation 406
 
1.7%
Close Punctuation 406
 
1.7%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2263
11.6%
t 2013
10.3%
r 1969
10.1%
a 1918
9.8%
e 1655
8.5%
o 1435
 
7.3%
l 1197
 
6.1%
c 1197
 
6.1%
s 1188
 
6.1%
d 1096
 
5.6%
Other values (9) 3626
18.5%
Uppercase Letter
ValueCountFrequency (%)
G 1102
60.2%
E 406
 
22.2%
F 305
 
16.7%
K 10
 
0.5%
O 5
 
0.3%
S 1
 
0.1%
P 1
 
0.1%
C 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 2
66.7%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
2034
100.0%
Open Punctuation
ValueCountFrequency (%)
( 406
100.0%
Close Punctuation
ValueCountFrequency (%)
) 406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21388
88.2%
Common 2849
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2263
10.6%
t 2013
 
9.4%
r 1969
 
9.2%
a 1918
 
9.0%
e 1655
 
7.7%
o 1435
 
6.7%
l 1197
 
5.6%
c 1197
 
5.6%
s 1188
 
5.6%
G 1102
 
5.2%
Other values (17) 5451
25.5%
Common
ValueCountFrequency (%)
2034
71.4%
( 406
 
14.3%
) 406
 
14.3%
/ 2
 
0.1%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2263
 
9.3%
2034
 
8.4%
t 2013
 
8.3%
r 1969
 
8.1%
a 1918
 
7.9%
e 1655
 
6.8%
o 1435
 
5.9%
l 1197
 
4.9%
c 1197
 
4.9%
s 1188
 
4.9%
Other values (22) 7368
30.4%

no_of_times_food_cooked_last_week
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
8 - 14 times
2040 
1-7 times
1386 
15 - 21 times
355 
Did not cook at home last week
230 
More than 21 times
 
52

Length

Max length30
Median length12
Mean length12.159734
Min length9

Characters and Unicode

Total characters49,405
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-7 times
2nd row15 - 21 times
3rd row8 - 14 times
4th row8 - 14 times
5th row1-7 times

Common Values

ValueCountFrequency (%)
8 - 14 times 2040
50.2%
1-7 times 1386
34.1%
15 - 21 times 355
 
8.7%
Did not cook at home last week 230
 
5.7%
More than 21 times 52
 
1.3%

Length

2024-12-06T11:25:07.560110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-06T11:25:07.651936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
times 3833
27.1%
2395
16.9%
8 2040
14.4%
14 2040
14.4%
1-7 1386
 
9.8%
21 407
 
2.9%
15 355
 
2.5%
did 230
 
1.6%
not 230
 
1.6%
cook 230
 
1.6%
Other values (6) 1024
 
7.2%

Most occurring characters

ValueCountFrequency (%)
10107
20.5%
e 4575
9.3%
t 4575
9.3%
1 4188
8.5%
i 4063
8.2%
m 4063
8.2%
s 4063
8.2%
- 3781
 
7.7%
8 2040
 
4.1%
4 2040
 
4.1%
Other values (15) 5910
12.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24819
50.2%
Decimal Number 10416
21.1%
Space Separator 10107
20.5%
Dash Punctuation 3781
 
7.7%
Uppercase Letter 282
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4575
18.4%
t 4575
18.4%
i 4063
16.4%
m 4063
16.4%
s 4063
16.4%
o 972
 
3.9%
a 512
 
2.1%
k 460
 
1.9%
n 282
 
1.1%
h 282
 
1.1%
Other values (5) 972
 
3.9%
Decimal Number
ValueCountFrequency (%)
1 4188
40.2%
8 2040
19.6%
4 2040
19.6%
7 1386
 
13.3%
2 407
 
3.9%
5 355
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
D 230
81.6%
M 52
 
18.4%
Space Separator
ValueCountFrequency (%)
10107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3781
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25101
50.8%
Common 24304
49.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4575
18.2%
t 4575
18.2%
i 4063
16.2%
m 4063
16.2%
s 4063
16.2%
o 972
 
3.9%
a 512
 
2.0%
k 460
 
1.8%
n 282
 
1.1%
h 282
 
1.1%
Other values (7) 1254
 
5.0%
Common
ValueCountFrequency (%)
10107
41.6%
1 4188
17.2%
- 3781
 
15.6%
8 2040
 
8.4%
4 2040
 
8.4%
7 1386
 
5.7%
2 407
 
1.7%
5 355
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10107
20.5%
e 4575
9.3%
t 4575
9.3%
1 4188
8.5%
i 4063
8.2%
m 4063
8.2%
s 4063
8.2%
- 3781
 
7.7%
8 2040
 
4.1%
4 2040
 
4.1%
Other values (15) 5910
12.0%

gas_used_for_cooking
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
True
3564 
False
499 
ValueCountFrequency (%)
True 3564
87.7%
False 499
 
12.3%
2024-12-06T11:25:07.746477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

electricity_from_national_grid_used_for_cooking
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
2494 
True
1569 
ValueCountFrequency (%)
False 2494
61.4%
True 1569
38.6%
2024-12-06T11:25:07.825318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

electricity_generated_using_solar_energy_used_for_cooking
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4016 
True
 
47
ValueCountFrequency (%)
False 4016
98.8%
True 47
 
1.2%
2024-12-06T11:25:07.902133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

firewood_used_for_cooking
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
3052 
True
1011 
ValueCountFrequency (%)
False 3052
75.1%
True 1011
 
24.9%
2024-12-06T11:25:07.976593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

kerosene_used_for_cooking
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4004 
True
 
59
ValueCountFrequency (%)
False 4004
98.5%
True 59
 
1.5%
2024-12-06T11:25:08.053652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

sawdust_or_paddy_husk_used_for_cooking
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4055 
True
 
8
ValueCountFrequency (%)
False 4055
99.8%
True 8
 
0.2%
2024-12-06T11:25:08.127716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

biogas_used_for_cooking
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4063 
ValueCountFrequency (%)
False 4063
100.0%
2024-12-06T11:25:08.200609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

coconut_shells_or_charcoal_used_for_cooking
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4062 
True
 
1
ValueCountFrequency (%)
False 4062
> 99.9%
True 1
 
< 0.1%
2024-12-06T11:25:08.271094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

other_methods_used_for_cooking
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
False
4010 
True
 
53
ValueCountFrequency (%)
False 4010
98.7%
True 53
 
1.3%
2024-12-06T11:25:08.344856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-12-06T11:25:03.202479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-06T11:25:08.435403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
aware_of_no_of_units_generated_by_solar_systemboil_water_before_drinkingcoconut_shells_or_charcoal_used_for_cookingdoes_water_heating_equipment_serve_other_housing_unitselectricity_from_national_grid_used_for_cookingelectricity_generated_using_solar_energy_used_for_cookingfirewood_used_for_cookinggas_used_for_cookinggenerate_electicity_using_bio_energygenerate_electicity_using_mini_hydropowergenerate_electicity_using_other_methodsgenerate_electicity_using_solar_energygenerate_electicity_using_wind_powerhave_backup_generatorhave_system_to_store_backup_energyhousehold_members_used_hot_water_last_weekkerosene_used_for_cookingmethod_of_receiving_waterno_of_times_food_cooked_last_weekno_of_units_generated_by_solar_systemother_methods_used_for_cookingsawdust_or_paddy_husk_used_for_cookingsolar_energy_used_for_agricultural_systemssolar_energy_used_for_all_abovesolar_energy_used_for_car_chargingsolar_energy_used_for_cookingsolar_energy_used_for_other_purposessolar_energy_used_for_outdoor_lightingsolar_energy_used_for_water_heatingsolar_system_invertor_or_noninvertorsolar_system_ongrid_or_offgirdsource_of_energy_for_boiling_drinking_waterwater_heating_method_for_bathingwhen_was_solar_system_installed
aware_of_no_of_units_generated_by_solar_system1.0000.0001.0000.0000.0000.1410.0670.0690.0000.0000.0001.0000.0000.0000.0810.0940.0350.0000.1371.0000.0001.0000.0000.0000.0000.0000.0720.1620.1360.2640.2840.1000.0710.000
boil_water_before_drinking0.0001.0000.0000.0510.0000.0350.0140.0170.0000.0000.0000.0350.0000.0000.0290.0970.0000.0290.1290.2080.0700.0000.0000.0000.0000.0300.0000.0000.0000.0000.0001.0000.1360.050
coconut_shells_or_charcoal_used_for_cooking1.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.000
does_water_heating_equipment_serve_other_housing_units0.0000.0511.0001.0000.0480.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0290.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.000
electricity_from_national_grid_used_for_cooking0.0000.0000.0000.0481.0000.0000.1690.1710.0000.0040.0000.1190.0180.0530.0520.0700.0160.0510.1230.0000.0880.0000.0000.0000.0000.0000.0000.0780.0000.0510.0540.2000.1890.000
electricity_generated_using_solar_energy_used_for_cooking0.1410.0350.0000.0000.0001.0000.0410.0210.0000.0000.0000.2440.0000.0420.1130.0590.0000.0360.0000.0000.0000.0000.0000.0970.0000.1780.0000.0000.0000.0960.0000.3990.2030.000
firewood_used_for_cooking0.0670.0140.0000.0020.1690.0411.0000.4440.0000.0000.0140.1750.0000.0700.1030.1070.0000.1720.0900.0000.0510.0420.0000.0000.0000.0000.0000.0240.0280.0430.0000.6210.2430.040
gas_used_for_cooking0.0690.0170.0000.0000.1710.0210.4441.0000.0240.0000.0050.0660.0000.0300.0480.0700.1390.1060.1680.0000.2900.0400.0000.0000.0000.0000.0000.0130.0000.0000.0000.5530.1270.000
generate_electicity_using_bio_energy0.0000.0000.0000.0000.0000.0000.0000.0241.0000.0310.0170.0000.0000.0000.0000.0190.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.000
generate_electicity_using_mini_hydropower0.0000.0000.0000.0000.0040.0000.0000.0000.0311.0000.0000.0000.0510.0400.0110.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1890.0560.000
generate_electicity_using_other_methods0.0000.0000.0000.0000.0000.0000.0140.0050.0170.0001.0000.0000.0000.0220.0660.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1570.0780.000
generate_electicity_using_solar_energy1.0000.0350.0000.0000.1190.2440.1750.0660.0000.0000.0001.0000.0000.2510.2470.2100.0050.0330.0601.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.3450.5731.000
generate_electicity_using_wind_power0.0000.0000.0000.0000.0180.0000.0000.0000.0000.0510.0000.0001.0000.0000.0000.0290.0000.0000.0281.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
have_backup_generator0.0000.0000.0000.0000.0530.0420.0700.0300.0000.0400.0220.2510.0001.0000.0900.0530.0000.0510.0570.0770.0000.0000.0000.0000.0000.0000.0000.0000.0350.0700.0730.1270.1830.094
have_system_to_store_backup_energy0.0810.0290.0000.0000.0520.1130.1030.0480.0000.0110.0660.2470.0000.0901.0000.0840.0000.0240.0220.1240.0000.0000.0000.0780.0000.0140.1010.0000.0000.1730.0000.1630.1980.000
household_members_used_hot_water_last_week0.0940.0971.0000.0440.0700.0590.1070.0700.0190.0000.0000.2100.0290.0530.0841.0000.0000.1010.0470.0000.0000.0000.0000.0000.0000.0000.1220.0000.0470.0000.1490.0930.3330.000
kerosene_used_for_cooking0.0350.0000.0000.0000.0160.0000.0000.1390.0000.0000.0000.0050.0000.0000.0000.0001.0000.0190.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4190.0330.013
method_of_receiving_water0.0000.0290.0000.0000.0510.0360.1720.1060.0000.0000.0000.0330.0000.0510.0240.1010.0191.0000.0310.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0620.053
no_of_times_food_cooked_last_week0.1370.1290.0000.0290.1230.0000.0900.1680.0390.0000.0320.0600.0280.0570.0220.0470.0120.0311.0000.0410.4400.0000.0000.0000.0000.0000.0590.1370.0000.0000.0840.0470.0570.031
no_of_units_generated_by_solar_system1.0000.2081.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0770.1240.0000.0000.0000.0411.0000.2561.0001.0000.0000.0000.1540.0000.0000.1540.0000.1500.1750.0000.000
other_methods_used_for_cooking0.0000.0700.0000.0000.0880.0000.0510.2900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.4400.2561.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0440.000
sawdust_or_paddy_husk_used_for_cooking1.0000.0000.0001.0000.0000.0000.0420.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.5770.0071.000
solar_energy_used_for_agricultural_systems0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.1650.2820.1360.0000.0580.0600.0000.0001.0000.0000.000
solar_energy_used_for_all_above0.0000.0001.0000.0000.0000.0970.0000.0000.0000.0000.0001.0000.0000.0000.0780.0000.0000.0000.0000.0000.0001.0000.1651.0000.0770.2440.0000.0240.1120.0320.2710.4400.1060.000
solar_energy_used_for_car_charging0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.2820.0771.0000.2470.0000.1230.1250.0000.1350.0000.0000.000
solar_energy_used_for_cooking0.0000.0301.0000.0000.0000.1780.0000.0000.0000.0000.0001.0000.0000.0000.0140.0000.0000.0000.0000.1540.0001.0000.1360.2440.2471.0000.0000.3640.4840.0000.3280.0000.0630.086
solar_energy_used_for_other_purposes0.0720.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.1010.1220.0000.0000.0590.0000.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.3770.1000.1440.000
solar_energy_used_for_outdoor_lighting0.1620.0001.0000.0000.0780.0000.0240.0130.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.1370.0000.0001.0000.0580.0240.1230.3640.0001.0000.1560.1750.6120.0760.2490.170
solar_energy_used_for_water_heating0.1360.0001.0000.0000.0000.0000.0280.0000.0000.0000.0001.0000.0000.0350.0000.0470.0000.0000.0000.1540.0001.0000.0600.1120.1250.4840.0000.1561.0000.1830.6010.0000.2130.000
solar_system_invertor_or_noninvertor0.2640.0001.0000.0000.0510.0960.0430.0000.0000.0000.0001.0000.0000.0700.1730.0000.0000.0000.0000.0000.0751.0000.0000.0320.0000.0000.0000.1750.1831.0000.2720.0000.1570.000
solar_system_ongrid_or_offgird0.2840.0001.0000.0000.0540.0000.0000.0000.0000.0000.0001.0000.0000.0730.0000.1490.0000.0000.0840.1500.0001.0000.0000.2710.1350.3280.3770.6120.6010.2721.0000.2310.1910.077
source_of_energy_for_boiling_drinking_water0.1001.0001.0000.0000.2000.3990.6210.5530.0000.1890.1570.3450.0000.1270.1630.0930.4190.0390.0470.1750.0000.5771.0000.4400.0000.0000.1000.0760.0000.0000.2311.0000.1600.160
water_heating_method_for_bathing0.0710.1360.0000.0470.1890.2030.2430.1270.0120.0560.0780.5730.0000.1830.1980.3330.0330.0620.0570.0000.0440.0070.0000.1060.0000.0630.1440.2490.2130.1570.1910.1601.0000.235
when_was_solar_system_installed0.0000.0501.0000.0000.0000.0000.0400.0000.0000.0000.0001.0000.0000.0940.0000.0000.0130.0530.0310.0000.0001.0000.0000.0000.0000.0860.0000.1700.0000.0000.0770.1600.2351.000

Missing values

2024-12-06T11:25:03.352066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-06T11:25:03.754827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

household_IDhave_backup_generatorgenerate_electicity_using_solar_energygenerate_electicity_using_bio_energygenerate_electicity_using_mini_hydropowergenerate_electicity_using_wind_powergenerate_electicity_using_other_methodssolar_system_ongrid_or_offgirdsolar_system_invertor_or_noninvertorsolar_energy_used_for_water_heatingsolar_energy_used_for_cookingsolar_energy_used_for_outdoor_lightingsolar_energy_used_for_car_chargingsolar_energy_used_for_agricultural_systemssolar_energy_used_for_all_abovesolar_energy_used_for_other_purposesaware_of_no_of_units_generated_by_solar_systemno_of_units_generated_by_solar_systemwhen_was_solar_system_installedhave_system_to_store_backup_energymethod_of_receiving_waterwater_heating_method_for_bathingdoes_water_heating_equipment_serve_other_housing_unitshousehold_members_used_hot_water_last_weekboil_water_before_drinkingsource_of_energy_for_boiling_drinking_waterno_of_times_food_cooked_last_weekgas_used_for_cookingelectricity_from_national_grid_used_for_cookingelectricity_generated_using_solar_energy_used_for_cookingfirewood_used_for_cookingkerosene_used_for_cookingsawdust_or_paddy_husk_used_for_cookingbiogas_used_for_cookingcoconut_shells_or_charcoal_used_for_cookingother_methods_used_for_cooking
0ID0001NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).NaNNoYesFirewood1-7 timesYesYesNoYesNoNoNoNoNo
1ID0002NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)None, we do not use hot water for bathing or body wash purposes.NaNNaNNoNaN15 - 21 timesYesNoNoNoNoNoNoNoNo
2ID0003NoYesNoNoNoNoYesYesNoNoNoNoNoNoNoYes1200.0Before November 2022YesTap Water (National Water Supply and drainage board)We have an in-built water heating system powered solely by the grid supply.NoYesYesElectricity (directly from the national grid)8 - 14 timesNoYesNoNoNoNoNoNoNo
3ID0004NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).NaNNoYesGas8 - 14 timesYesNoNoNoNoNoNoNoNo
4ID0005NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (Local Government Organizations)None, we do not use hot water for bathing or body wash purposes.NaNNaNYesElectricity (directly from the national grid)1-7 timesYesNoNoNoNoNoNoNoNo
5ID0006NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)None, we do not use hot water for bathing or body wash purposes.NaNNaNNoNaNMore than 21 timesYesNoNoNoNoNoNoNoNo
6ID0007NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)None, we do not use hot water for bathing or body wash purposes.NaNNaNYesElectricity (directly from the national grid)8 - 14 timesYesNoNoNoNoNoNoNoNo
7ID0008NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)We don't have an inbuilt system for water heating, we use heated water through an electric kettle/heater.NaNNoYesElectricity (directly from the national grid)More than 21 timesYesYesNoNoNoNoNoNoNo
8ID0009NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).NaNNoYesFirewood8 - 14 timesNoNoNoYesNoNoNoNoNo
9ID0010NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)None, we do not use hot water for bathing or body wash purposes.NaNNaNYesFirewood8 - 14 timesNoYesNoYesNoNoNoNoNo
household_IDhave_backup_generatorgenerate_electicity_using_solar_energygenerate_electicity_using_bio_energygenerate_electicity_using_mini_hydropowergenerate_electicity_using_wind_powergenerate_electicity_using_other_methodssolar_system_ongrid_or_offgirdsolar_system_invertor_or_noninvertorsolar_energy_used_for_water_heatingsolar_energy_used_for_cookingsolar_energy_used_for_outdoor_lightingsolar_energy_used_for_car_chargingsolar_energy_used_for_agricultural_systemssolar_energy_used_for_all_abovesolar_energy_used_for_other_purposesaware_of_no_of_units_generated_by_solar_systemno_of_units_generated_by_solar_systemwhen_was_solar_system_installedhave_system_to_store_backup_energymethod_of_receiving_waterwater_heating_method_for_bathingdoes_water_heating_equipment_serve_other_housing_unitshousehold_members_used_hot_water_last_weekboil_water_before_drinkingsource_of_energy_for_boiling_drinking_waterno_of_times_food_cooked_last_weekgas_used_for_cookingelectricity_from_national_grid_used_for_cookingelectricity_generated_using_solar_energy_used_for_cookingfirewood_used_for_cookingkerosene_used_for_cookingsawdust_or_paddy_husk_used_for_cookingbiogas_used_for_cookingcoconut_shells_or_charcoal_used_for_cookingother_methods_used_for_cooking
4053ID4054NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)None, we do not use hot water for bathing or body wash purposes.NaNNaNYesGas8 - 14 timesYesYesNoNoNoNoNoNoNo
4054ID4055NoYesNoNoNoNoYesYesNoNoNoNoNoNoNoYes100.0Before November 2022NoTap Water (National Water Supply and drainage board)We have an in-built water heating system powered solely by solar energy.NoYesYesGas1-7 timesYesYesYesNoNoNoNoNoNo
4055ID4056NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).NaNNoNoNaN8 - 14 timesYesNoNoYesNoNoNoNoNo
4056ID4057NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)None, we do not use hot water for bathing or body wash purposes.NaNNaNNoNaN8 - 14 timesNoNoNoYesNoNoNoNoNo
4057ID4058NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNYesTap Water (National Water Supply and drainage board)We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).NaNNoYesFirewood8 - 14 timesYesNoNoYesNoNoNoNoNo
4058ID4059NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).NaNYesYesGas15 - 21 timesYesYesNoNoNoNoNoNoNo
4059ID4060NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoOtherNone, we do not use hot water for bathing or body wash purposes.NaNNaNYesFirewood1-7 timesNoNoNoYesNoNoNoNoNo
4060ID4061NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)None, we do not use hot water for bathing or body wash purposes.NaNNaNNoNaN1-7 timesNoNoNoNoYesNoNoNoNo
4061ID4062NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)We don't have an inbuilt system for water heating, we use water heated by means such as gas, firewood etc. (other than electric kettle, electric water heater or water heated using any other electrical appliance).NaNNoYesGas1-7 timesYesNoNoNoNoNoNoNoNo
4062ID4063NoNoNoNoNoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoTap Water (National Water Supply and drainage board)None, we do not use hot water for bathing or body wash purposes.NaNNaNYesGas1-7 timesYesNoNoNoNoNoNoNoNo